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Comparison of Estimation Techniques for a Forest Inventory in which Double Sampling for Stratification Is Used

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A number of different estimators can be used when forest inventory plots cover two or more distinctly different condition classes. In this article the properties of two approximate Horvitz-Thompson (HT) estimators, a ratio of means (RM), and a mean of ratios (MR) estimator are explored in the framework of double sampling for stratification. Relevant theoretical properties are given, and a simulation study is used to compare the properties of these estimators. It is concluded that the variance of the RM estimator is smaller than that of the approximate HT for the populations studied, but that of the MR estimator is larger. While ratio estimators are known to be biased for small sample sizes, an additional bias also occurs when the population of interest has an edge-effect. The RM classical variance estimator underestimated the true variance in the simulation study. The approximate HT estimators exhibited biases that were generally smaller than those of the RM and MR estimators for populations with an edge-effect. Its classical variance estimator was essentially unbiased. FOR. SCI. 47(4):563–576.
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Keywords: Horvitz-Thompson estimator; Ratio of means estimator; environmental management; forest; forest management; forest resources; forestry; forestry research; forestry science; mean of ratios estimator; natural resource management; natural resources

Document Type: Miscellaneous

Affiliations: USDA Forest Service, Rocky Mountain Research Station, 2150 A Center Drive, Fort Collins, Colorado, 80526-2098, Phone: (970) 295-5974; Fax: (970) 295-5927 [email protected]

Publication date: 2001-11-01

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    Forest Science is a peer-reviewed journal publishing fundamental and applied research that explores all aspects of natural and social sciences as they apply to the function and management of the forested ecosystems of the world. Topics include silviculture, forest management, biometrics, economics, entomology & pathology, fire & fuels management, forest ecology, genetics & tree improvement, geospatial technologies, harvesting & utilization, landscape ecology, operations research, forest policy, physiology, recreation, social sciences, soils & hydrology, and wildlife management.
    Forest Science is published bimonthly in February, April, June, August, October, and December.

    2016 Impact Factor: 1.782 (Rank 17/64 in forestry)

    Average time from submission to first decision: 62.5 days*
    June 1, 2016 to Feb. 28, 2017

    Also published by SAF:
    Journal of Forestry
    Other SAF Publications
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